1. Field of the Invention
The present invention relates to monitoring and management of electrical power transmission and distribution networks, and more particularly to a system and method for determining the grid state and transmission line capacity of such a network by determining the network load flow using a deterministic, non-iterative, real time analysis of the network.
2. Description of the Background
The global electric industry is facing a number of challenges: an aging infrastructure, growing demand, and rapidly changing markets, all of which threaten to reduce the reliability of the electricity supply. Currently, deregulation of the electricity supply industry continues, although somewhat more cautiously than before due to California's recent experience. Deregulation and the drive to increase efficiencies in power systems have been particularly relevant in the attempt to develop new processes for intelligent observation and management of the grid.
Increasing demand due to economic and demographic variations, without additional generation investments, has led transmission and distribution systems worldwide to their limits of reliable operation. According to the North American Electric Reliability Council (NAERC), transmission congestion is expected to continue over the next decade. Growth in demand and the increasing number of energy transactions continue to outstrip the proposed expansion of transmission system. In the same line, the Edison Electric Institute indicates that the U.S. transmission system requires nearly $56 billion in new investment over the next decade, but only $35 billion is likely to be spent. Figures from the Federal Energy Regulatory Commission (FERC) place the total transmission congestion costs nationwide at several hundred million dollars.
One action FERC is taking to improve coordination on the US grid is to create Regional Transmission Organizations (RTOs). Yet, even this important step towards nationwide coordination raises concerns about transmission reliability. In its report, “Reliability Assessment 2001-2010,” the NAERC stated, “The transition period from existing grid operation arrangements to the new world of RTO-managed grids may create some negative system reliability impacts. New system and organizational structures will need to be implemented over very aggressive time lines.” Furthermore, the Transmission Rights market is just beginning. In the US FERC, as a result of three conferences, issued a working paper where the important characteristics were defined: LMP (Location Marginal Pricing) as the system for congestion management, the availability of a non-discriminatory standard “Network Access Service,” RTO operation bid based day ahead and spot markets, holder's ability to sell transmission rights, and mitigation through market bidding rules.
Therefore, today more than ever before, the need exists for adequate methods for determining the basic functions that provide System Operators and Regional Transmission Organization managers with the best knowledge on their existing grid. Tools that help reduce the uncertainty or “fuzzy-zone” for safety operations with accurate computation of the grid state and transmission lines capacity are therefore required.
The primary objective of operation and security management is to maximize infrastructure use while concurrently reducing the risk of system instability and blackouts. One specific type of transmission system voltage instability is the slow spreading uncontrollable decline in voltage known as voltage collapse.
Electricity providers try to avoid power disruption to their customers. From a momentary interruption to a full blackout, any disturbance is costly to the provider and consumers alike. Six days of rolling blackouts in 2001 cost Silicon Valley businesses more than $1 billion according to the San Jose Mercury News. A report released by the Electric Power Research Institute's (EPRI) Consortium for Electrical Infrastructure to Support a Digital Society (CEIDS) notes that U.S. businesses lose over $45 billion annually from outages.
The electrical power network is represented through the power system model by means of an accurate representation of all of its components: bus bars, lines, transformers, loads, generators, DC couplings, shunts, etc. These elements are modeled using a set of values defining its state (voltage, angle, and active and reactive power for nodal elements and complex flows for link elements). These values are not independent. They must satisfy the Ohm and Kirchov Laws, which for these variables becomes a system of non-linear equations.
This system of equations well known as the Load Flow or Power Flow equations can or cannot have a solution (Voltage Collapse) and the mathematical solution to this problem normally is multiple, with a degree of multiplicity as high as 2N where N is the number of buses in the network. From this set of 2N solutions, only one corresponds to the physical situation. The rest of the solutions are spurious and cannot represent the physical solution of a real power system. A standard approach to this highly nonlinear problem has been the use of numerical approximation methods.
The topology of the actual representation can vary if the model is only detailed up to bus bar level, which may suit off-line studies for Planning Engineers. Yet for operations, the model must reach switching levels. Modeling for other purposes can also be done, as described in U.S. Pat. No. 6,202,041 to Tse et al., which discloses a modeling method for small perturbation stability, as well as U.S. Pat. No. 6,141,634 to Flint et al., which discloses an AC power line network simulator.
Real time instruments in the field measure some of these parameters that are sent through communication lines to centralized control centers. SCADA (Supervisory and Data Acquisition) Systems are the basic hardware-software basis for observation and operation of a power system network (alarms, Automatic Generation Control or “AGC,” etc.), and EMSs (Energy Management Systems) include more advanced software applications that implement the process of information transformation within such control centers calculating load flow, optimal power flow, contingency analysis, etc. For example, U.S. Pat. No. 5,181,026 to Granville discloses a system for measuring voltage, phase angle, and line temperatures in power lines.
A power system model with a complete set of exact measurements for all parameters is not possible; hence, observation of real values is limited to a subset of all needed parameters. The remaining values must be estimated. Therefore, to a given set of real time values at an instant t are added the corresponding complementary estimated values. In order to represent a feasible electrical state of the power system, these values must satisfy the Load Flow equations. Hence, at the heart of any real time system modeling lie two basic processes: state estimation and load flow equations solving methods.
Most state estimation methods today define an external model (being the neighboring power systems' topology and values) and propagate voltage values to the internal model that of the given power system. It is a least square function minimization process of the differences between the real measured values and the estimated values.
The standard methodology for solving the load flow equations problem has been to use the Fast Decoupled Newton-Raphson (FDNR) algorithm. This methodology presents two majors drawbacks:                a) Even in the case where there is a solution, FDNR may not be able to find it, due to the fractal nature of the convergence region of this algorithm. This is inherent to the iterative nature of the Newton-Raphson Methodology.        b) FDNR cannot assure that a solution (one that solves the mathematical equations) really represents the physical one. Newton-Raphson can jump from the neighborhood of one solution to the neighborhood of another in an uncontrollable way.        
The problems of the FDNR methodology are well known by the electrical sector, taking the form of stochastic non-convergence or dependency of the result in the order of the actions over the network.
Several attempts to overcome these difficulties have been undertaken in the past, but with limited success. For example, load flow and state estimators currently used in electrical advanced applications at control centers represent the state-of-the-art technology: Newton-Raphson Iterative methodology, as well as variants for improving convergence and speed of computation (Fast decoupling, etc.), avoiding triangulation of the Jacobian, as well as new approaches using fuzzy logic and genetic algorithms.
An interesting approach to the load flow equations solutions is the use of the General Homotopy Method proposed by Okumura et al., “A computation of Power System Characteristic by General Homotopy and Investigation of its Stability”, in which is defined a family of solutions characterized by a continuous real parameter defining a path. The path must be followed step-by-step using a predictor-corrector methodology involving Newton-Raphson in each step. In order to avoid the inherent Newton-Raphson drawbacks, the step size must be carefully chosen in order not to lose the path to the solution.
Other approximations to the load flow equation solutions include Tolikas et al. “Homotopy Methods for solving Decoupled Power Flow equations” or Guo et al., “The Homotopy Continuation Method to Approach Voltage Collapse of Electric Power Systems”. Any of the approaches results are reliable enough and efficient for industrial use in a real time environment.
The list of references on this matter is not exhaustive but its length indicates that it is a problem yet to be solved to complete satisfaction.
Once the model of the power system is validated as an accurate one (model topology has been improved and quality of measurements has been attained or at best ranked adequately), through state estimation and load flow calculation, many other processes typically take place within an EMS operator working environment, including:
1) Limit violation control of parameters outside operating limits. These processes may comprise intelligent methods that generate proposed remedial actions by means of using load flow on the last estimated snapshot or state of the power system by the EMS State Estimator automatically (by means of an algorithm) or manually using a real time network simulation by the operator. Some physical devices, such as protections and others with or without local intelligence, have also been developed as alternatives, including U.S. Pat. No. 5,428,494 to Ahuja, which presents a system for over-voltage and under-current protection, and U.S. Pat. No. 5,327,355 to Chiba et al., which presents a fuzzy logic based method for tap transformer settings for voltage control. Extreme remedial action always involve load shedding, which process is treated in some form in U.S. Pat. No. 4,324,987 to Sullivan, II et al., U.S. Pat. No. 4,337,401 to Olson, U.S. Pat. No. 4,583,182 to Breddan, and U.S. Pat. No. 5,414,640 to Seem. A method for controlling voltage and reactive power fluctuations in adjacent power systems is discussed in U.S. Pat. No. 6,188,205 to Tanimoto et al.
2) Planned maintenance outages assessment through instant real time on line simulation from the actual network state.
3) Optimal power flow for objective functions such as losses minimization through reactive power cycling.
4) Voltage stability analysis, which can be viewed as the aggregation of the following:                a. PV and QV curves construction.        b. Determination of voltage collapse point and current operating point as well as voltage stability criterion.        c. Generating a metric to voltage collapse. One such example is the margin to voltage collapse defined as the largest load change that the power system may sustain at a set of buses from a well-defined operating point, as described in U.S. Pat. No. 5,745,368 to Ejebe et al.        d. Voltage stability assessment and contingency analysis and classification. Concerning voltage stability security assessment, state of the art load flow methodologies in general do not work. A well detailed explanation on which of these tasks they tend to fail can be found on U.S. Pat. Nos. 5,594,659 and 5,610,834 to Schlueter. Because of this, Newton-Raphson is ill conditioned for the situation. In the cited patents, Schlueter states that current methods lack diagnosis procedures for determining causes of specific voltage instability problems, as well as intelligent preventive procedures. His method is an attempt to overcome this situation in certain cases. He provides for detecting if certain contingencies (line outages and loss of generation) related to reactive reserve basins can cause voltage instability.        
Another approach is that given in U.S. Pat. No. 5,642,000 to Jean-Jumeau et al. where a performance index is related to the load demand and not to voltage for the first time. This index allows for determining the amount of load increase the system can tolerate before the collapse, and when collapse is to be originated by a contingency, it gives a measure of its severity. It overcomes the computational burden of the high non-linearity of order 2N by inventing a new characteristic linear equation of the exact saddle-node bifurcation point of order N: “Decoupled, parameter-dependent, non-linear (DPDN) dynamic systems as ones whose dynamics can be represented by a set of non-linear equations with a varying parameter that can de decoupled from the remainder of the equation”.
A method in U.S. Pat. No. 4,974,140 to Iba et al. discloses discriminating voltage stability from the method of multiple load flow solutions.
Also, U.S. Pat. No. 5,745,368 to Ejebe et al. compares three approaches to determining an alternative voltage collapse point and an index, using a comparison of the method introduced: the Test Function Method (TFM) with two other prior art existing methods, namely, Continuation Power Flow (CPF) and Multiple Load Flow Method (MLF).
Other approaches that are innovative yet still inefficient include those of U.S. Pat. No. 5,629,862 to Brandwajn et al. using artificial intelligence rule-based systems, or U.S. Pat. No. 5,625,751 to Brandwajn et al. for contingency ranking.                e. Future near-term dynamic voltage stability. One such example for a near term definition of 25 minutes is U.S. Pat. No. 5,796,628 to Chiang et al. where system voltage profiles are predicted and loads and contingencies are analyzed on this near-term scenario in terms of load margins to collapse. Continuation load flow technique CPFLOW (predictor corrector type of continuation power flow with a step-size control) through the nose of PV QV curves (saddle-node bifurcation) is reported to work without numerical difficulties. Yet, the patent preferred embodiment describes the sensitivity of the number of final iterations to the attainment of a good approximation point for the next solution by the predictor. It is also stated that good step-size controls are usually custom-designed for specific applications. So again, there is some craftsmanship as in all PV QV curve construction using any derived method from Newton-Raphson iterative process.        
5) On-Line transient stability. This is a more ambitious task, entering the realm of the differential equations where the right hand term is a load flow equation. U.S. Pat. No. 5,638,297 to Mansour et al. defines via an artificial contingency on-line transient stability assessment.
6) Load forecast. We list here this process even though it is not related to load flow methodologies because knowing the forecasted load profile will help in many instances while analyzing future contingencies and generating action plans (limits back to normal, restoration). Standard methodologies used by successful methods include the more classical autoregressive methods (ARIMA) Box Jenkins time series approach, as well as more recent artificial intelligence neural network approaches.
7) Disturbance detection and restoration.                a. For distribution grids, the problem is simpler and well known. Restoration can be managed through a set of rules (small expert systems) since the topology is radial. State of the art is mostly centered in fault location and its resolution as well as protection schemes by different standard and creative ways. Patents include U.S. Pat. No. 5,303,112 to Zulaski et al., U.S. Pat. No. 5,455,776 to Novosel, U.S. Pat. No. 5,568,399 to Sumic, U.S. Pat. No. 5,684,710 to Ehlers et al., U.S. Pat. No. 5,973,899 to Williams et al., U.S. Pat. No. 6,185,482 to Egolf et al., and U.S. Pat. No. 6,347,027 to Nelson et al.        b. For transmission grids, the restoration problem has not been solved satisfactorily as a general universal procedure valid for any power system network. With ageing infrastructures and growing demand, disturbances are increasingly likely to happen. Traditionally, restoration after a disturbance has been one of the most difficult things for electrical companies to handle. While hundreds of hours of systems analysis and documentation go into restoration plans, they never match the reality of any specific disturbance, and they are dynamic in nature. Automatic tools for helping operators have been attempted. Avoiding the need for local rules specification would be desired. Detection is related to intelligent alarm and topology changes processing. Restoration plan validation by the operator requires load flow calculation after each step in order to guarantee a feasible electrical network state after each and every action, with the post-disturbance steady state as initial condition of the action plan.        
As we have seen, all of the above central processes need a working, real-time load flow method. The methodologies on which the industry has based, up until now, the on-line real time monitoring and managing of networks as well as off-line analysis tools for planning, programming, and for investing decisions support, generally behave well under certain continuity of the network condition. Iterative in nature, they need initial points near the solution or equivalent knowledge of the previous solution to compute the next stage in a real time environment. This last aspect is responsible for not being able to behave well when disruptions of the system state take place, when a major disturbance or blackout takes place. To conclude, we add that when the electrical network state is close to voltage collapse, precisely when operators and planners need the support of these tools the most, traditional methods fail and frequently cannot deliver a correct calculation.